calc_rate.int: Extract rates from multiple replicates in intermittent-flow...

In januarharianto/respR: Import, Process, Analyse, and Calculate Rates from Respirometry Data

Extract rates from multiple replicates in intermittent-flow respirometry data

Description

calc_rate.int allows you to extract an oxygen uptake or production rate from multiple replicates in intermittent-flow respirometry. It allows you to easily use consistent selection parameters to extract a single rate from each replicate, for example a specific time range or row range.

Usage

calc_rate.int(
  x,
  starts = NULL,
  wait = NULL,
  measure = NULL,
  by = "row",
  plot = TRUE,
  ...
)

Arguments

x	Object of class inspect or data.frame. This is the timeseries of paired values of oxygen against time containing multiple replicates from which to calculate rates.
starts	Integer(s). Row locations or times of the start of each replicate. A single value input indicates a regular interval in rows or time units starting at the first row of the data in x. If replicates do not cycle at a regular interval, a vector of the row or time of each replicate can be entered. The function assumes each replicate ends at the row preceding the start of the next replicate, or for the final replicate the final row of the dataset.
wait	Numeric. Rows or time period to exclude at the start of each replicate. Default is NULL in which case no wait phase is applied. See Details.
measure	Numeric. Rows or time period over which to calculate rate in each replicate. Applied directly after wait phase. Default is NULL in which case the entire replicate is used. See Details.
by	String. "row" or "time". Defaults to "row". Method by which starts, wait and measure are applied.
plot	Logical. Default is TRUE. Plots the results. See 'Plotting' section for details.
...	Allows additional plotting controls to be passed, such as type, pos, legend, and quiet.

Details

calc_rate.int uses the starts input to subset each replicate. The wait and measure inputs control which parts of each replicate data are excluded and included from the rate calculation. It extracts a rate from each replicate using these, and saves it and other data to a summary table.

The x input should be aninspect object. Alternatively, it can be a two-column data frame containing paired values of time and oxygen from an intermittent-flow experiment in columns 1 and 2 respectively (though we always recommend processing such data in inspect() first). If a multiple column dataset is entered as x the first two columns are selected by default. If these are not the intended data use inspect to select the correct time and oxygen columns.

Specifying replicate structure

The starts input specifies the locations of the start of each replicate in the data in x. This can be in one of two ways:

• A single numeric value specifying how replicates are spaced starting from the data in the first row. This option should only be used when replicates cycle at regular intervals. This can be a regular row or time interval, as specified via the by input. If the first replicate does not start at row 1, the data should be subset so that it does (see subset_data()) and example here.

• A numeric vector of row locations or times, as specified via the by input, of the start of each individual replicate. The first replicate does not have to start at the first row of the data, and all data after the last entry is assumed to be part of the final replicate. Regular R syntax such as seq(), 1:10, etc. is also accepted, so can be used to specify both regular and irregular replicate spacing.

For both methods it is assumed each replicate ends at the row preceding the start of the next replicate, or in the case of the last replicate the final row of the dataset.

Specifying rate region

The wait and measure inputs are used to specify the region from which to extract a rate and exclude flush periods. They can be entered as row intervals or time values in the units of the input data. The wait phase controls the amount of data at the start of each replicate to be ignored, that is not used in rate calculations. The measure phase determines the region after this over which a rate is calculated. There is no flush phase input since this is assumed to be from the end of the measure phase to the end of the replicate.

Both wait and measure can be entered in one of two ways:

• Single numeric values specifying a row width or a time period, as specified via the by input. Use this if you want to use the same wait and measure phases in every replicate, that is extract a rate from the same region of each.

• If starts is a vector of locations of the start of each replicate, these inputs can also be vectors of equal length of row lengths or time periods as specified via the by input. This is only useful if you want to use different wait and/or measure phases in different replicates.

If wait = NULL no wait phase is applied. If measure = NULL the rate is extracted from the start of the replicate or end of the wait phase to the last row of the replicate. This will typically include the flush period, so is rarely what you would want. Similarly if any measure input is beyond the available values in the replicate the closest value (row or time) is used instead, which again would typically be the last row of the replicate.

Example

See examples below for actual code, but here is a simple example. An experiment comprises replicates which cycle at ten minute intervals with data recorded every second. Therefore each replicate will be 600 rows long. Flushes of the respirometer take 3 minutes at the end of each replicate. We want to exclude the first 2 minutes (120 rows) of data in each, and measure an oxygen uptake rate for five minutes (300 rows), leaving the three minutes of flushing (180 rows) excluded. The inputs for this would be:

⁠starts = 600, wait = 120, measure = 300, by = "row"⁠

More details

Only a single rate can be extracted from each replicate. If for some reason you need to extract multiple rates from single replicates use subset_data() and calc_rate() which accepts multiple from and to inputs. Similarly, the calc_rate method of by = "oxygen" is not supported in calc_rate.int. See vignettes on the website for examples of alternative ways of iterating calc_rate across multiple replicates if you need to get around these constraints.

Plot

If plot = TRUE (the default), the result for each replicate is plotted on a grid up to a maximum of 20. Which replicates are plotted can be selected using the pos input (default is 1:20), either in the main function call or when calling plot() on output objects.

There are three ways of plotting the results, which can be selected using the type input:

• type = "rep": The default. Each individual replicate is plotted with the rate region (i.e. measure phase) highlighted in yellow. The wait and measure phases are also highlighted with red and green backgrounds respectively. These are also labelled if legend = TRUE.

• type = "full": Each replicate rate (i.e. measure phase) is highlighted in the context of the whole dataset. May be quite difficult to interpret if dataset is large.

• type = "cr": Plots individual replicate results as calc_rate objects.

For all types pos can be used to select which replicate(s) to plot.

S3 Generic Functions

Saved output objects can be used in the generic S3 functions plot(), print(), summary(), and mean().

• plot(): plots the result. See Plot section above.

• print(): prints the result of a single replicate, by default the first. Others can be printed by passing the pos input. e.g. print(x, pos = 2)

• summary(): prints summary table of all results and metadata, or the rows specified by the pos input. e.g. summary(x, pos = 1:5). The ⁠$rep⁠ column indicates the replicate number. The summary table can be exported as a separate data frame by passing export = TRUE.

• mean(): calculates the mean of the rates from every replicate, or the rows specified by the pos input. e.g. mean(x, pos = 1:5) The mean can be exported as a numeric value by passing export = TRUE.

More

For additional help, documentation, vignettes, and more visit the respR website at https://januarharianto.github.io/respR/

Value

Output is a list object of class calc_rate.int containing a calc_rate object for each replicate in ⁠$results⁠. The output also contains a ⁠$summary⁠ table which includes the full rate regression results from each replicate with replicate number indicated by the ⁠$rep⁠ column. Output also contains a ⁠$rate⁠ element which contains the rate values from each replicate in order. The function call, inputs, and other metadata are also included. Note, that if you have many replicates this object can be rather large (several MB).

Examples

# Irregular replicate structure ------------------------------------------

# Prepare the data to use in examples
# Note in this dataset each replicate is a different length!
data <- intermittent.rd
# Convert time to minutes (to show different options below)
data[[1]] <- round(data[[1]]/60, 2)
# Inspect
urch_insp <- inspect(data)

# Calculate rate across each entire replicate
# This leads to erroneous rates because the flush is included
calc_rate.int(urch_insp,
              starts = c(1, 2101, 3901))

# So instead we also specify a 'measure' phase
calc_rate.int(urch_insp,
              starts = c(1, 2101, 3901),
              measure = 1000)

# You can even specify different 'measure' phases in each rep
calc_rate.int(urch_insp,
              starts = c(1, 2101, 3901),
              measure = c(1500, 1200, 200))

# We usually don't want to use the start of a replicate just after the flush,
# so we specify a 'wait' phase. We can also specify 'starts', 'wait' and
# 'measure' in units of time instead of rows.
#
# By time
# (this time we save the result)
urch_res <- calc_rate.int(urch_insp,
                          starts = c(0, 35, 65), # start locations in minutes
                          wait = 2,              # wait for 2 mins
                          measure = 10,          # measure for 10 mins
                          by = "time")

# Regular replicate structure --------------------------------------------

# If replicates cycle at regular intervals, 'starts' can be used to specify
# the spacing in rows or time, starting at row 1. Therefore data must be
# subset first so that the first replicate starts at row 1.
#
# Subset and inspect data
zeb_insp <- zeb_intermittent.rd %>%
  subset_data(from = 5840,
              to = 75139,
              by = "row",
              quiet = TRUE) %>%
  inspect()

# Calculate a rate from same 6-minute region in every replicate.
# Replicates cycle at every 660 rows.
zeb_res <- calc_rate.int(zeb_insp,
                         starts = 660,
                         wait = 120, # exclude first 2 mins
                         measure = 360, # rate from 6 mins after 'wait'
                         plot = TRUE)

# S3 functions ------------------------------------------------------------

# Outputs can be used in print(), summary(), and mean().
# 'pos' can be used to select replicate ranges
summary(zeb_res)
mean(zeb_res, pos = 1:5)

# There are three ways by which the results can be plotted.
# 'pos' can be used to select replicates to be plotted.
#
# type = "rep" - the default. Each replicate plotted on a grid with rate
# region highlighted (up to a maximum of 20).
plot(urch_res)

# type = "full" - each replicate rate region plotted on entire data series.
plot(urch_res, pos = 1:2, type = "full")
# Of limited utility when datset is large
plot(zeb_res, pos = 10, type = "full")

# type = "cr" - the 'calc_rate' object for selected replicates in 'pos' is plotted
plot(urch_res, pos = 2, type = "cr")

# See vignettes on website for how to adjust and convert rates from calc_rate.int

januarharianto/respR documentation built on April 20, 2024, 4:34 p.m.